Can I use experimental data to solve equations with integrals?

In summary, the conversation is about how to obtain a certain value from an equation that contains an integral using experimental data. The suggested approach is to plot G(f) v. f and [G(f)]^2 v. f and use numerical integration techniques to calculate B. It is also mentioned that multiplying G's with their corresponding f's and taking the sum is not the proper approach for numerical integration.
  • #1
rakue
1
0
Hi, I don't know if this is the proper part of the forum to ask this, but I'm trying to figure out how I can obtain a certain value from an equation that contains an integral if I want to use experimental data. To keep it simple, the equation looks like this:

B= (∫G(f)df)2 / (∫G(f)2df)

where it is to be evaluated from 0 to ∞.

But to keep my question simple, say, my equation is: B= ∫G(f)df

Now, I have experimental values for "G(f)" and it's corresponding "f" (I'm not even sure if I'm understanding this correctly).

Is it right for me to simply, multiply the G's with their corresponding f's and then take the sum? So in effect, I'm basically doing this:

Ʃ(Gi*fi) where i is the index for each value of f

Am I still integrating if I do this? If not, what's the proper approach to this?

Thank you very much for all your help.
 
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  • #2
Based on the equations you have written, why not plot G(f) v. f and [G(f)]^2 v. f ?

If these plots turn out to be smooth curves, there are a number of numerical integration techniques which you can use to calculate B.
 
  • #3
rakue said:
Is it right for me to simply, multiply the G's with their corresponding f's and then take the sum? So in effect, I'm basically doing this:

Ʃ(Gi*fi) where i is the index for each value of f

Am I still integrating if I do this? If not, what's the proper approach to this?

What do you mean by the index for each value of f? I think what you're referring to is incorrect. Numerical integration techniques basically rely on one or another method for calculating the area under a curve, so you need some measure of "width."

In the simplest case, [itex] \int G(x) dx [/itex] could be approximated by [itex] \sum{G(x_{i}) \Delta x} [/itex] where [itex] \Delta x [/itex] is an appropriate measure of the "width" of a column whose height is determined by [itex] G(x_{i}) [/itex]. Usually this would just be: [itex] \Delta x = (x_{i+1}-x_{i}) [/itex] Graphically, it would look like approximating the area under the curve with a set of vertical bars (this is a pretty common teaching tool for basic calculus: the actual integral is basically the limit as [itex] \Delta x \rightarrow dx [/itex], at least when we're talking about a Riemann integral).

With that said, that method is very rudimentary (although that might be what you're looking for, depending on the context of your question) and is rarely, if ever, used in actual numerical computations or data analysis. If you're using any kind of data analysis software (i.e. MATLAB) there are much more accurate techniques for numerical integration.
 

FAQ: Can I use experimental data to solve equations with integrals?

How do you integrate experimental data?

Integrating experimental data involves combining and analyzing data from different experiments or sources to gain a better understanding of a particular phenomenon or research question. This can be done using statistical techniques, data visualization tools, and various software programs.

Why is it important to integrate experimental data?

Integrating experimental data allows researchers to see patterns and connections that may not be apparent when looking at individual experiments. It also helps to validate findings and can lead to new insights and discoveries.

What are some challenges of integrating experimental data?

Some challenges of integrating experimental data include differences in data collection methods, varying levels of data quality, and the need for data standardization. Additionally, integrating data from different disciplines or fields can also pose challenges.

What are the best practices for integrating experimental data?

Some best practices for integrating experimental data include clearly defining the research question or objective, using standardized data formats and protocols, and verifying the accuracy and completeness of the data. It is also important to carefully select the appropriate statistical methods and tools for the integration process.

How can integrated experimental data be used in research?

Integrated experimental data can be used in various ways in research, such as identifying new relationships and patterns, validating findings, and generating new hypotheses. It can also be used for data mining and machine learning applications to make predictions and inform decision making.

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